1. Field of the Invention
The present invention refers to a method such as that illustrated in FIG. 1, for example, for the adjustment of settings of a hearing aid and to a hearing aid comprising software to implement a method for the adjustment of settings of the aid.
2. Description of Related Art Including Information Disclosed Under 37 CFR 1.97 and 1.98
So called self-learning hearing aids are known, where the adaptation of optimized settings is automatically executed by the hearing aid itself.
A drawback or problem exists in recognising valid or true modifications made by the user.
No modifications of the settings for a long period does not explicitly means, that the user is happy with the respective settings. It might well be, that the user is not familiar with the manipulation of settings of the hearing aid or the settings are such, that the user can live with the settings but they are not optimized.
Today's high end hearing instruments incorporate sophisticated schemes to automatically adjust the instrument parameters to specific acoustic environments. They hereby provide optimized sound qualities and speech perception in all situations. The current techniques have still some drawbacks in terms of fulfilling individual needs and preferences of the hearing instrument users, as mentioned above. In order to get more insight to these individual requirements data logging has become an interesting tool while reporting all the users' interactions with the hearing instruments to the fitter. There are existing hearing aids, which can automatically analyse the data log stored in the non-volatile memory of the hearing instrument and provide some changes to the current settings. The fitter can either accept the proposed modifications or make changes him/herself. Most of the times these modifications yield to an improved comfort for the hearing instrument users since interactions with the hearing instrument tend be needed less often than prior to the modified adjustments.
It is a disadvantage of the current actual solutions that modifications have to be done either by the fitter or audiologist since the user can't neither reprogram the hearing aid himself/herself nor allocate the hearing instrument to update its setting based on frequent user interactions. To overcome these shortcomings the hearing instrument should learn out of user interactions and optimize settings automatically, “User preference learning” has yet been developed. Data logging is still the basic tool for the procedure; learning algorithms will exploit the data gathered over time within different acoustical environments. The results are now interpreted in the hearing instrument and directly applied, a visit of the fitter or audiologist is no more needed and this is a great advantage.
This improved method still has some drawbacks; the performance and validity of the embedded learning rules depends to a large extend on user interactions. The more interactions there are the faster and better learning converges. A couple of single interactions would not really train the system efficiently. Since hearing instruments incorporate different programs, training has to be done for all accordingly. It might therefore take long until the user gets a real benefit out of his/her self-learning hearing instrument and this must be overcome.
In addition many changes in settings made by the user does not automatically mean, that the initial settings were bad. Vice versa as stated above no changes in settings does not automatically means, that the settings are good.